Skip to main content
  • Main Menu
  • Utility Menu
  • Search

WAVE Research Group

Water, Atmosphere, Vegetation, and Extremes

WAVE Research Group
  • Home
  • Bio
  • Research
  • Projects
  • Members
  • Meetings
  • Publications
  • Resources
    • Courses
    • Blog

Decision tree and random forest in Matlab

August 15, 2020

created: Yizhou Zhuang, 08/15/2020

last edited: Yizhou Zhuang, 08/15/2020

 

decision tree for regression: https://www.mathworks.com/help/stats/fitrtree.html#butl1ll_head

decision tree for classification: https://www.mathworks.com/help/stats/fitctree.html#butluiw_head

Improving trees and how trees split: https://www.mathworks.com/help/stats/improving-classification-trees-and-regression-trees.html#mw_b859ab75-0be6-4523-acf6-5fdbb1f23d15

decision tree template: https://www.mathworks.com/help/stats/templatetree.html

selecting predictor for random forest: https://www.mathworks.com/help/stats/select-predictors-for-random-forests.html?s_tid=srchtitle

Ensemble learning (RF basics): https://www.mathworks.com/help/stats/framework-for-ensemble-learning.html

Ensemble algorithms: https://www.mathworks.com/help/stats/ensemble-algorithms.html

How to fit ensemble of learners: https://www.mathworks.com/help/stats/fitrensemble.html

How to test ensemble quality: https://www.mathworks.com/help/stats/methods-to-evaluate-ensemble-quality.html

Blog posts by month

  • January 2021 (1)
  • November 2020 (2)
  • August 2020 (5)
RSS Feed Subscribe https://dept.atmos.ucla.edu/rongfu/feed
Admin Login
OpenScholar